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# Autogenerated By   : src/main/python/generator/generator.py
# Autogenerated From : scripts/builtin/pnmf.dml

from typing import Dict, Iterable

from systemds.operator import OperationNode, Matrix, Frame, List, MultiReturn, Scalar
from systemds.script_building.dag import OutputType
from systemds.utils.consts import VALID_INPUT_TYPES


def pnmf(X: Matrix,
         rnk: int,
         **kwargs: Dict[str, VALID_INPUT_TYPES]):
    """
     The pnmf-function implements Poisson Non-negative Matrix Factorization (PNMF). Matrix X is factorized into two
     non-negative matrices, W and H based on Poisson probabilistic assumption. This non-negativity makes the resulting
     matrices easier to inspect.
    
     [Chao Liu, Hung-chih Yang, Jinliang Fan, Li-Wei He, Yi-Min Wang:
     Distributed nonnegative matrix factorization for web-scale dyadic 
     data analysis on mapreduce. WWW 2010: 681-690]
    
    
    
    :param X: Matrix of feature vectors.
    :param rnk: Number of components into which matrix X is to be factored.
    :param eps: Tolerance
    :param maxi: Maximum number of conjugate gradient iterations.
    :param verbose: If TRUE, 'iter' and 'obj' are printed.
    :return: List of pattern matrices, one for each repetition.
    :return: List of amplitude matrices, one for each repetition.
    """

    params_dict = {'X': X, 'rnk': rnk}
    params_dict.update(kwargs)
    
    vX_0 = Matrix(X.sds_context, '')
    vX_1 = Matrix(X.sds_context, '')
    output_nodes = [vX_0, vX_1, ]

    op = MultiReturn(X.sds_context, 'pnmf', output_nodes, named_input_nodes=params_dict)

    vX_0._unnamed_input_nodes = [op]
    vX_1._unnamed_input_nodes = [op]

    return op
